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Specialist vs LLM
Same task, two systems, quality, latency and £ on a frozen set.
Beyond “call the LLM”: tokenisation, sequence labelling, retrieval text, evaluation, and when a specialist model still beats a general one.
Created by Baljeet Dogra
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Same task, two systems, quality, latency and £ on a frozen set.
Lab
NER or classification with error analysis, not a leaderboard screenshot.
Expand a part for the syllabus. Content stays searchable when closed.
Tokenisers, multilingual penalties, off-by-one structured output.
Classifiers, NER, span labelling, class imbalance.
Chunking, queries, hybrid features — the NLP under RAG.
Metrics for generation, specialist vs LLM, capstone report.
Search, support, documents. An LLM is not automatically the system.
You want Hugging Face without cargo-cult pipelines.
Related: Deep Learning Specialisation · Production Generative AI Systems
Working depth, not a from-scratch GPT. Deep Learning Specialisation covers more of the internals.
It is the lab stack. You should be able to leave it.
Six weeks to NLP you can evaluate against a general LLM. Create an account to enrol.
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